The structure of gravel-bed flow with intermediate submergence: a laboratory study

The paper reports an experimental study of the flow structure over an immobile gravel bed in open channel at intermediate submergence, with particular focus on the near-bed region. The experiments consisted of velocity measurements using three-component (stereoscopic) Particle Image Velocimetry (PIV) in near-bed horizontal plane and two-component PIV in three vertical planes that covered three distinctly different hydraulic scenarios where the ratio of flow depth to roughness height (i.e., relative submergence) changes from 7.5 to 10.8. Detailed velocity measurements were supplemented with fine-scale bed elevation data obtained with a laser scanner. The data revealed longitudinal low-momentum and high-momentum "strips'' in the time-averaged velocity field, likely induced by secondary currents. This depth-scale pattern was superimposed with particle-scale patches of flow heterogeneity induced by gravel particle protrusions. A similar picture emerged when considering second-order velocity moments. The interaction between the flow field and gravel-bed protrusions is assessed using cross correlations of velocity components and bed elevations in a horizontal plane just above gravel particle crests. The cross correlations suggest that upward and downward fluid motions are mainly associated with upstream-facing and lee sides of particles, respectively. Results also show that the relative submergence affects the turbulence intensity profiles for vertical velocity over the whole flow depth, while only a weak effect, limited to the near-bed region, is noticed for streamwise velocity component. The approximation of mean velocity profiles with a logarithmic formula reveals that log-profile parameters depend on relative submergence, highlighting inapplicability of a conventional "universal'' logarithmic law for gravel-bed flows with intermediate submergence

Quantifying national household air pollution (HAP) exposure to PM2.5 in rural and urban areas

According to WHO (World Health Organization), in 2020, 14% of people in global urban areas relied on polluting solid fuels and technologies, compared with 52% of the rural population. The health impacts of such inequality are massive. It was estimated that 3.2 million premature deaths per year (2020), particularly in low-income and middle-income countries due to household air pollution (HAP). Several studies provide estimates of the exposure to fine particulate matter (PM2.5) from household air pollution (HAP-PM2.5) for users of different fuel/cookstove types in rural and urban areas. However, hardly any studies estimate the population-weighted exposure to HAP-PM2.5 at the global scale. A Bayesian hierarchical model was developed to estimate PM2.5 exposure coefficients and their uncertainties for an annual average of HAP-PM2.5 personal exposure. The predicted HAP-PM2.5 exposure at the user level was used to estimate the national-level exposure for the population living in urban and rural areas. The results suggest that switching from polluting solid fuels (biomass, charcoal, coal) to cleaner fuels (gas and electricity) for heating and cooking can potentially reduce the national-level HAP-PM2.5 personal exposure on average by 53%. However, there exists a significant disparity between rural and urban areas, partly reflecting inequality in energy access. More specifically, switching from polluting solid fuels for heating and cooking to cleaner fuels can reduce the personal exposure to HAP-PM2.5 in rural areas by 54% and in urban areas by 38%. The study indicates that increased access to clean fuels and improved stove interventions are needed to achieve the goals of universal energy access and equality between urban and rural areas

Multi Target Optimization of Turbojet Engine with Multi Target Genetic Algorithm

Abstract: In this paper, Turbojet engine will be optimized in ideal condition by multi target genetic algorithm. Target functions are specific thrust (ST), specific fuel consumption (SFC) and thermal efficiency (η t ) that will be optimized simultaneously according to design variables and in two by two way and their Pareto points will be showed. Design variables included inlet Mach number and total compressor pressure ratio. Then according to Pareto points important relations between target functions will be introduced. It is obvious that these relations without using these methods are inaccessible

An open-source automatic survey of green roofs in London using segmentation of aerial imagery

Green roofs can mitigate heat, increase biodiversity, and attenuate storm water, giving some of the benefits of natural vegetation in an urban context where ground space is scarce. To guide the design of more sustainable and climate-resilient buildings and neighbourhoods, there is a need to assess the existing status of green roof coverage and explore the potential for future implementation. Therefore, accurate information on the prevalence and characteristics of existing green roofs is needed, but this information is currently lacking. Segmentation algorithms have been used widely to identify buildings and land cover in aerial imagery. Using a machine learning algorithm based on U-Net (Ronneberger et al., 2015) to segment aerial imagery, we surveyed the area and coverage of green roofs in London, producing a geospatial dataset (https://doi.org/10.5281/zenodo.7603123, Simpson et al., 2023). We estimate that there was 0.23 km2 of green roof in the Central Activities Zone (CAZ) of London, 1.07 km2 in Inner London, and 1.89 km2 in Greater London in the year 2021. This corresponds to 2.0 % of the total building footprint area in the CAZ and 1.3 % in Inner London. There is a relatively higher concentration of green roofs in the City of London, covering 3.9 % of the total building footprint area. Test set accuracy was 0.99, with an F score of 0.58. When tested against imagery and labels from a different year (2019), the model performed just as well as a model trained on the imagery and labels from that year, showing that the model generalised well between different imagery. We improve on previous studies by including more negative examples in the training data and by requiring coincidence between vector building footprints and green roof patches. We experimented with different data augmentation methods and found a small improvement in performance when applying random elastic deformations, colour shifts, gamma adjustments, and rotations to the imagery. The survey covers 1558 km2 of Greater London, making this the largest open automatic survey of green roofs in any city. The geospatial dataset is at the single-building level, providing a higher level of detail over the larger area compared to what was already available. This dataset will enable future work exploring the potential of green roofs in London and on urban climate modelling.</p

Plantar pressure distribution in diverse stages of diabetic neuropathy

Background: Diabetic Foot Ulceration in patients with diabetes could be associated with high plantar pressure caused by diabetes neuropathy. Therefore, it seems that one of the ways of identifying high-risk legs in diabetic patients with neuropathy would be characterization of elevated plantar pressure distributions. Objective: Comparing the plantar pressure distribution in diabetic patients who suffered neuropathy with those without neuropathy. Methods and materials: Plantar pressure distribution was recorded in the following categories: 38 diabetic patients without neuropathy, 30, 40 and 34 patients with mild neuropathy, moderate and severe neuropathy respectively. Results: Patients suffered from severe neuropathy suggested higher maximum peak plantar pressure at midfoot, heel, and medial forefoot. The peak pressure of midfoot was significantly different in the following categories as well: patient without neuropathy (32.3 ± 17.9 kPa), mild neuropathic (24.0 ± 17.9 kPa), moderate neuropathic (21.5 ± 12.6 kPa), and severe neuropathic (22.9 ± 10.7 kPa) groups (p = 0.02). Conclusion: The progression of diabetic neuropathy would have been increased followed by the peak plantar pressure. © 2019, Springer Nature Switzerland AG

No influence of amyloid-beta-degrading neprilysin activity on prion pathogenesis.

Transmissible spongiform encephalopathies are characterized by the accumulation of PrPSc, a protease-resistant form of a host-derived protein termed PrPC. Substantial evidence indicates that PrPSc represents an essential component of the infectious agent, which is termed prion. The accumulation of PrPSc within the central nervous system of prion-infected organisms is a dynamic process that is regulated both by production and by clearance of PrPSc. Although several proteases have been implicated in proteolysis of PrPC, the mechanisms underlying proteolysis of PrPSc remain unclear. Here, it was investigated whether neprilysin, a metalloprotease known to degrade extracellular amyloidogenic proteins such as amyloid-beta, plays a role in prion pathogenesis in vivo. As neprilysin has a broad substrate specificity and is localized subcellularly in the vicinity of PrP, it represents a plausible candidate for prion degradation. Prions were therefore administered to mice lacking or overexpressing neprilysin in brain. However, the gene dosage of neprilysin did not modulate accumulation of PrPSc in brain. Also, incubation times and clinical course of prion disease, as well as brain infectivity titres at terminal stage, were unaffected. These data rule out neprilysin as a major modulator of PrPSc accumulation and prion pathogenesis

Effects of intravenous Semelil (ANGIPARS�) on diabetic foot ulcers healing: A multicenter clinical trial

Some diabetic foot ulcers, which are notoriously difficult to cure, are one of the most common health problems in diabetic patients .There are several surgical and medical options which already have been introduced for treatment of diabetic foot ulcers, so some patient will require amputation. The purpose of this study was to evaluate the efficacy of intravenous Semelil (ANGIPARS�), a naive herbal extract to accelerate healing of diabetic foot ulcers. A multi-centric randomized controlled trial was conducted to evaluate intravenous Semelil for healing of diabetic foot ulcers. Sixteen diabetic patients were treated with intravenous Semelil, and nine other patients were treated with placebo as control group. Both groups were otherwise treated by wound debridement and irrigation with normal saline solution, systemic antibiotic therapy and daily wound dressing. Before and after intervention, the foot ulcer surface area was measured, by digital photography, mapping and planimetry. After 4 weeks, the mean foot ulcer surface area decreased from 479.93±379.75 mm2 to 198.93±143.75 mm2 in the intervention group (p = 0.000) and from 766.22±960.50 mm2 to 689.11±846.74 mm2 in the control group (p = 0.076). Average wound closure in the treatment group was significantly greater than placebo group (64 vs. 25, p= 0.015). This herbal extract by intravenous rout in combination with conventional therapy is more effective than conventional therapy by itself probably without side effect. However, further studies are required in the future to confirm these results in larger population

A tool for assessing the climate change mitigation and health impacts of environmental policies: the Cities Rapid Assessment Framework for Transformation (CRAFT) [version 2; peer review: 3 approved]

Background: A growing number of cities, including Greater London, have set ambitious targets, including detailed policies and implementation plans, to reach global goals on sustainability, health, and climate change. Here we present a tool for a rapid assessment of the magnitude of impact of specific policy initiatives to reach these targets. The decision-support tool simultaneously quantifies the environmental and health impacts of specified selected policies. Methods: The ‘Cities Rapid Assessment Framework for Transformation (CRAFT)’ tool was applied to Greater London. CRAFT quantifies the effects of ten environmental policies on changes in (1) greenhouse gas (GHG) emissions, (2) exposures to environmental hazards, (3) travel-related physical activity, and (4) mortality (the number of attributable deaths avoided in one typical year). Publicly available data and epidemiological evidence were used to make rapid quantitative estimates of these effects based on proportional reductions in GHG emissions and environmental exposures from current baseline levels and to compute the mortality impacts. Results: The CRAFT tool estimates that, of roughly 50,000 annual deaths in Greater London, the modelled hazards (PM2.5 (from indoor and outdoor sources), outdoor NO2, indoor radon, cold, overheating) and low travel-related physical activity are responsible for approximately 10,000 premature environment-related deaths. Implementing the selected polices could reduce the annual mortality number by about 20% (~1,900 deaths) by 2050. The majority of these deaths (1,700) may be avoided through increased uptake in active travel. Thus, out of ten environmental policies, the ‘active travel’ policy provides the greatest health benefit. Also, implementing the ten policies results in a GHG reduction of around 90%. Conclusions: The CRAFT tool quantifies the effects of city policies on reducing GHG emissions, decreasing environmental health hazards, and improving public health. The tool has potential value for policy makers through providing quantitative estimates of health impacts to support and prioritise policy options

Quantifying national household air pollution (HAP) exposure to PM2.5 in rural and urban areas

According to WHO (World Health Organization), in 2020, 14% of people in global urban areas relied on polluting solid fuels and technologies, compared with 52% of the rural population. The health impacts of such inequality are massive. It was estimated that 3.2 million premature deaths per year (2020), particularly in low-income and middle-income countries due to household air pollution (HAP). Several studies provide estimates of the exposure to fine particulate matter (PM2.5) from household air pollution (HAP-PM2.5) for users of different fuel/cookstove types in rural and urban areas. However, hardly any studies estimate the population-weighted exposure to HAP-PM2.5 at the global scale. A Bayesian hierarchical model was developed to estimate PM2.5 exposure coefficients and their uncertainties for an annual average of HAP-PM2.5 personal exposure. The predicted HAP-PM2.5 exposure at the user level was used to estimate the national-level exposure for the population living in urban and rural areas. The results suggest that switching from polluting solid fuels (biomass, charcoal, coal) to cleaner fuels (gas and electricity) for heating and cooking can potentially reduce the national-level HAP-PM2.5 personal exposure on average by 53%. However, there exists a significant disparity between rural and urban areas, partly reflecting inequality in energy access. More specifically, switching from polluting solid fuels for heating and cooking to cleaner fuels can reduce the personal exposure to HAP-PM2.5 in rural areas by 54% and in urban areas by 38%. The study indicates that increased access to clean fuels and improved stove interventions are needed to achieve the goals of universal energy access and equality between urban and rural areas